Jayhawks

GP: 78 | W: 44 | L: 20 | OTL: 14 | P: 102
GF: 302 | GA: 264 | PP%: 20.96% | PK%: 76.72%
DG: Yves Létourneau | Morale : 50 | Moyenne d'Équipe : 54
Prochain matchs #1223 vs Sharks
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Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur C L R D CON CK FG DI SK ST EN DU PH FO PA SC DF PS EX LD PO MO OV TA SPÂgeContratSalaire Moyen
1Nico Sturm (R)X100.00817789747767696176595867554444050610243925,000$
2Joachim Blichfeld (R)X100.00736983666972756450596564624444050600213736,666$
3John QuennevilleXXX100.00746679737261606178516167624747050590231560,000$
4Kieffer BellowsX100.00727174697177825950476762644444050590212925,000$
5Luke Philp (R)X100.00726588676560606075516562624444050580231560,000$
6Adam MascherinX100.00757380617366705350515162484444050550212650,000$
7Tanner Laczynski (R)X100.00565282677463765564544651486060050550223925,000$
8Jonathan Dugan (R)X100.00615659677161745362524454465858050540213525,000$
9Scott Reedy (R)X100.00545189667561755156375549585454050530201560,000$
10Maxim Shalunov (R)X100.00324040406031313240323240363230050360261560,000$
11David Cotton (R)X100.00323737377131313237323237343230050350221525,000$
12Jordan Oesterle (C)X100.007242927767738263255247692561610506402712,200,000$
13Brogan Rafferty (R)X100.00787292647264666025604565434444050600241650,000$
14Michael Anderson (R)X100.00767189777163674925414161394444050580203925,000$
15Leon Gawanke (R)X100.00767286637268725425524163394444050580204796,667$
16Jeremy Davies (R)X100.00706679686664675225494159394444050560223925,000$
17Dmitri Samorukov (R)X100.00777485627465704625374061384444050560203825,000$
Rayé
1Artyom Zub (R)X100.0045454545454545454545454545454501430244925,000$
2Jack Ahcan (R)X100.00414545455539394145414145433230050410222825,000$
MOYENNE D'ÉQUIPE100.0064607463686064524647485747454504754
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Gardien CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SP
1Kirill Ustimenko (R)100.0059716670555959595758585454050590
2Kevin Lankinen (R)100.004545454545454545454545454501440
Rayé
MOYENNE D'ÉQUIPE100.005258565850525252515252505002652
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur Nom de l'ÉquipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Kieffer BellowsJayhawks (KC )LW705630864130016113942110528313.30%18119317.05771428570003408149.18%6100051.44010001452
2Nico SturmJayhawks (KC )C743634702350101142282947619512.24%24109114.7456113087000007060.90%149100011.2811010452
3Brogan RaffertyJayhawks (KC )D6811566733420189107140531117.86%142157323.1471118791680220151310.00%000000.8500000405
4Luke PhilpJayhawks (KC )C4930295921100401482025515314.85%1381816.7046102067000005155.47%117000011.4400000401
5Joachim BlichfeldJayhawks (KC )RW7827295672401421473431022387.87%23123715.872793890000004147.42%9700000.9000000232
6Michael AndersonJayhawks (KC )D78839472761021792145511005.52%141154119.7741014631390000112000.00%000000.6100011012
7Jordan OesterleJayhawks (KC )D4643236-214075738131574.94%7279017.1802251200001000.00%000000.9100000112
8Leon GawankeJayhawks (KC )D61729361950101517055223612.73%96107817.680000000000020.00%000000.6700011023
9John QuennevilleJayhawks (KC )C/LW/RW551915341220062701494710212.75%155409.8200010000002160.81%27300001.2601000122
10Adam MascherinJayhawks (KC )LW5381826162810673710225717.84%1563712.02000120001490041.30%4600000.8200011201
11Tanner LaczynskiJayhawks (KC )C5131922146013378122493.70%761212.0100000000000058.55%19300000.7200000010
12Jonathan DuganJayhawks (KC )LW4331013312069456425574.69%650911.8400000000001146.88%3200000.5100000011
13Dmitri SamorukovJayhawks (KC )D26279-31605411163512.50%2439415.180000000015100.00%000000.4600000002
14Scott ReedyJayhawks (KC )C2853832015233893113.16%429010.3700000000000050.00%13000000.5500000000
15Jeremy DaviesJayhawks (KC )D2614521202218342202.94%1928911.1300000000000025.00%800000.3500000001
16Maxim ShalunovJayhawks (KC )RW41012001120050.00%06516.4700000000000025.00%400000.3000000000
17David CottonJayhawks (KC )C1210100001320050.00%1968.0500000000000041.90%10500000.2100000000
18Stephen JohnsKansas CityD1000-100700210.00%12323.350000200003000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne82322235457619239240139912592169630150910.24%6211278315.53294978265628022536431857.01%361000070.9013043322126
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Gardien Nom de l'ÉquipeGP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA ST BG S1 S2 S3
1Kirill UstimenkoJayhawks (KC )784219130.9223.22454319124431450220.676377801174
Stats d'équipe Total ou en Moyenne784219130.9223.22454319124431450220.676377801174


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du Joueur Nom de l'ÉquipePOS Âge Date de Naissance Nouveau Joueur Poids Taille Non-Échange Disponible pour Échange Ballotage Forcé Contrat Type Salaire Actuel Salaire RestantCap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Adam MascherinJayhawks (KC )LW211998-06-05No206 Lbs5 ft11NoNoNo2Pro & Farm650,000$24,462$65,000$2,446$No650,000$Lien
Artyom ZubJayhawks (KC )D241995-10-03 17:43:45Yes198 Lbs6 ft2NoNoNo4Pro & Farm925,000$34,811$92,500$3,481$No925,000$925,000$925,000$
Brogan RaffertyJayhawks (KC )D241995-05-28Yes192 Lbs6 ft2NoNoNo1Pro & Farm650,000$24,462$65,000$2,446$NoLien
David CottonJayhawks (KC )C221997-07-09Yes204 Lbs6 ft3NoNoNo1Pro & Farm525,000$19,758$52,500$1,976$NoLien
Dmitri SamorukovJayhawks (KC )D201999-06-16Yes196 Lbs6 ft3NoNoNo3Pro & Farm825,000$31,048$82,500$3,105$No825,000$825,000$Lien
Jack AhcanJayhawks (KC )D221997-05-18Yes184 Lbs5 ft8NoNoNo2Pro & Farm825,000$31,048$82,500$3,105$No825,000$Lien
Jeremy DaviesJayhawks (KC )D221996-12-04Yes181 Lbs5 ft11NoNoNo3Pro & Farm925,000$34,811$92,500$3,481$No925,000$925,000$Lien
Joachim BlichfeldJayhawks (KC )RW211998-07-16Yes180 Lbs6 ft2NoNoNo3Pro & Farm736,666$27,723$73,667$2,772$No736,666$736,666$Lien
John QuennevilleJayhawks (KC )C/LW/RW231996-04-16No195 Lbs6 ft1YesNoNo1Pro & Farm560,000$21,075$56,000$2,108$NoLien
Jonathan DuganJayhawks (KC )LW211998-03-24Yes192 Lbs6 ft2NoNoNo3Pro & Farm525,000$19,758$52,500$1,976$No525,000$525,000$Lien
Jordan OesterleJayhawks (KC )D271992-06-24No182 Lbs6 ft0YesNoNo1Pro & Farm2,200,000$82,795$220,000$8,280$NoLien
Kevin LankinenJayhawks (KC )G241995-04-28 17:36:52Yes185 Lbs6 ft2NoNoNo4Pro & Farm800,000$30,107$80,000$3,011$No800,000$800,000$800,000$
Kieffer BellowsJayhawks (KC )LW211998-06-10No196 Lbs6 ft0NoNoNo2Pro & Farm925,000$34,811$92,500$3,481$No925,000$Lien
Kirill UstimenkoJayhawks (KC )G201999-01-29Yes187 Lbs6 ft3NoNoNo3Pro & Farm825,833$31,079$82,583$3,108$No825,833$825,833$Lien
Leon GawankeJayhawks (KC )D201999-05-31Yes198 Lbs6 ft1NoNoNo4Pro & Farm796,667$29,982$79,667$2,998$No796,667$796,667$796,667$
Luke PhilpJayhawks (KC )C231995-11-05Yes181 Lbs5 ft10YesNoNo1Pro & Farm560,000$21,075$56,000$2,108$NoLien
Maxim ShalunovJayhawks (KC )RW261993-01-31Yes185 Lbs6 ft3YesNoNo1Pro & Farm560,000$21,075$56,000$2,108$NoLien
Michael AndersonJayhawks (KC )D201999-05-25Yes196 Lbs6 ft0NoNoNo3Pro & Farm925,000$34,811$92,500$3,481$No925,000$925,000$Lien
Nico SturmJayhawks (KC )C241995-05-02Yes207 Lbs6 ft3NoNoNo3Pro & Farm925,000$34,811$92,500$3,481$No925,000$925,000$Lien
Scott ReedyJayhawks (KC )C201999-04-04Yes205 Lbs6 ft2YesNoNo1Pro & Farm560,000$21,075$56,000$2,108$NoLien
Tanner LaczynskiJayhawks (KC )C221997-06-01Yes205 Lbs6 ft1NoNoNo3Pro & Farm925,000$34,811$92,500$3,481$No925,000$925,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2122.24193 Lbs6 ft12.33816,627$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kieffer BellowsNico SturmMaxim Shalunov40122
2Adam MascherinLuke PhilpJoachim Blichfeld30122
3Jonathan DuganDavid CottonScott Reedy20122
4Jonathan DuganScott ReedyJohn Quenneville10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jordan OesterleBrogan Rafferty40122
2Dmitri SamorukovMichael Anderson30122
3Jeremy DaviesLeon Gawanke20122
4Leon Gawanke10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
160122
2Nico SturmJoachim Blichfeld40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Michael Anderson40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Michael Anderson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
16012260122
240122Michael Anderson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Leon Gawanke60122
2Michael Anderson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Tirs de Pénalité
, , , ,
Gardien
#1 : Kirill Ustimenko, #2 :


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
LigueDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1Admirals4200010121183210001001082210000011110160.7502138590013010565715210561029103374162462611515746.67%13376.92%01376284448.38%1335301344.31%625136345.85%184912911900560998501
2Baby Hawks32100000972220000007431010000023-140.66791524001301056571141056102910337410737297613215.38%11281.82%01376284448.38%1335301344.31%625136345.85%184912911900560998501
3Bears2110000079-2110000003211010000047-320.50071219001301056577410561029103374762914529222.22%6433.33%01376284448.38%1335301344.31%625136345.85%184912911900560998501
4Bruins2010000147-31010000024-21000000123-110.250481200130105657771056102910337496352156500.00%8275.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
5Cabaret Lady Mary Ann220000001138110000005231100000061541.000112031001301056571361056102910337494298706116.67%40100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
6Caroline22000000963110000004221100000054141.000917260013010565756105610291033747619638100.00%30100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
7Chiefs31100100910-11010000024-22100010076130.500915240013010565785105610291033741654434837114.29%15566.67%01376284448.38%1335301344.31%625136345.85%184912911900560998501
8Chill3200000113103210000018621100000054150.8331323360013010565790105610291033741323216698225.00%8275.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
9Comets31200000914-51010000024-221100000710-320.3339162500130105657105105610291033741262920518112.50%10460.00%11376284448.38%1335301344.31%625136345.85%184912911900560998501
10Cougars22000000633110000003211100000031241.0006111700130105657761056102910337477222046700.00%10190.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
11Crunch210000016601000000145-11100000021130.750681400130105657721056102910337484341254400.00%6266.67%01376284448.38%1335301344.31%625136345.85%184912911900560998501
12Heat440000002213922000000104622000000129381.000222951001301056571901056102910337415650161001218.33%80100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
13Las Vegas523000001116-52110000045-131200000711-440.4001119300013010565715810561029103374215616011317211.76%20385.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
14Manchots2200000013211110000005051100000082641.00013253801130105657941056102910337476208607457.14%30100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
15Marlies20100001812-41000000156-11010000036-310.25081624001301056579010561029103374973012448337.50%5260.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
16Minnesota3200010018992100010013671100000053250.8331830480013010565722410561029103374108411069200.00%50100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
17Monarchs411001011618-220100001811-32100010087140.5001629450013010565716510561029103374219694611212433.33%20765.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
18Monsters22000000844110000004221100000042241.000815230013010565794105610291033746522104010110.00%5180.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
19Monsters210000011082110000008531000000123-130.750102030001301056576610561029103374882714473133.33%7442.86%11376284448.38%1335301344.31%625136345.85%184912911900560998501
20Oceanics2010010058-3000000000002010010058-310.250581300130105657671056102910337410230656500.00%30100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
21Oil Kings403001001117-62010010069-32020000058-310.1251121321013010565714410561029103374167412412111218.18%10370.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
22Phantoms2020000059-41010000024-21010000035-200.000591410130105657751056102910337488191456500.00%7185.71%01376284448.38%1335301344.31%625136345.85%184912911900560998501
23Rocket2110000078-1110000005411010000024-220.50071320001301056577510561029103374104211846300.00%9366.67%01376284448.38%1335301344.31%625136345.85%184912911900560998501
24Senators22000000945110000004311100000051441.0009172600130105657891056102910337479218408337.50%40100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
25Sharks31000011131122100000110911000001032150.833132336001301056571041056102910337410831238410660.00%9366.67%01376284448.38%1335301344.31%625136345.85%184912911900560998501
26Sound Tigers220000001055110000005411100000051441.000101828001301056579010561029103374842313508112.50%4175.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
27Spiders21100000811-3110000006421010000027-520.500814220013010565783105610291033748925144311327.27%70100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
28Stars32100000981211000006601100000032140.6679182700130105657138105610291033741061914717114.29%5180.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
29Thunder21000010532100000103211100000021141.000551000130105657801056102910337463221746400.00%50100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
Total78422000628302264383922800315158128303920120031314413681020.65430253283421130105657315210561029103374327494454219592294820.96%2325476.72%21376284448.38%1335301344.31%625136345.85%184912911900560998501
30Wolf Pack220000001055110000004131100000064241.0001020300013010565789105610291033746516951300.00%20100.00%01376284448.38%1335301344.31%625136345.85%184912911900560998501
_Since Last GM Reset78422000628302264383922800315158128303920120031314413681020.65430253283421130105657315210561029103374327494454219592294820.96%2325476.72%21376284448.38%1335301344.31%625136345.85%184912911900560998501
_Vs Conference40231200302147128192012500201796217201170010168662510.6381472523991013010565716391056102910337416734742859851011211.88%1232877.24%21376284448.38%1335301344.31%625136345.85%184912911900560998501

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
78102SOL130253283431523274944542195921
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7842200628302264
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
392280315158128
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
3920120313144136
Derniers 10 Matchs
WLOTWOTL SOWSOL
540001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2294820.96%2325476.72%2
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
10561029103374130105657
Mises en Jeu
Gagnées en Zone OffensiveTotal en Zone Offensive% Gagnées en Zone Offensive Gagnées en Zone DéfensiveTotal en Zone Défensive% Gagnées en Zone DéfensiveGagnées en Zone NeutreTotal en Zone Neutre% Gagnées en Zone Neutre
1376284448.38%1335301344.31%625136345.85%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
184912911900560998501


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
2 - 2020-10-2312Jayhawks5Admirals6LXXSommaire du Match
4 - 2020-10-2526Bruins4Jayhawks2LSommaire du Match
9 - 2020-10-3057Las Vegas4Jayhawks2LSommaire du Match
11 - 2020-11-0172Jayhawks2Monsters3LXXSommaire du Match
14 - 2020-11-0487Jayhawks4Oceanics5LXSommaire du Match
16 - 2020-11-06104Chill2Jayhawks5WSommaire du Match
18 - 2020-11-08115Senators3Jayhawks4WSommaire du Match
21 - 2020-11-11136Jayhawks6Wolf Pack4WSommaire du Match
23 - 2020-11-13146Jayhawks5Sound Tigers1WSommaire du Match
24 - 2020-11-14157Jayhawks2Spiders7LSommaire du Match
27 - 2020-11-17177Jayhawks2Crunch1WSommaire du Match
29 - 2020-11-19191Rocket4Jayhawks5WSommaire du Match
32 - 2020-11-22214Monsters5Jayhawks8WSommaire du Match
34 - 2020-11-24223Jayhawks2Oil Kings3LSommaire du Match
35 - 2020-11-25231Jayhawks6Heat4WSommaire du Match
37 - 2020-11-27246Monsters2Jayhawks4WSommaire du Match
39 - 2020-11-29259Minnesota5Jayhawks4LXSommaire du Match
41 - 2020-12-01270Jayhawks4Bears7LSommaire du Match
42 - 2020-12-02275Jayhawks4Chiefs2WSommaire du Match
44 - 2020-12-04289Jayhawks5Minnesota3WSommaire du Match
46 - 2020-12-06301Heat2Jayhawks4WSommaire du Match
48 - 2020-12-08317Monarchs4Jayhawks3LXXSommaire du Match
51 - 2020-12-11343Marlies6Jayhawks5LXXSommaire du Match
53 - 2020-12-13350Jayhawks4Monarchs5LXSommaire du Match
54 - 2020-12-14364Oil Kings5Jayhawks3LSommaire du Match
57 - 2020-12-17386Admirals7Jayhawks6LXSommaire du Match
59 - 2020-12-19399Jayhawks1Las Vegas6LSommaire du Match
60 - 2020-12-20411Sharks5Jayhawks7WSommaire du Match
63 - 2020-12-23429Jayhawks4Monsters2WSommaire du Match
65 - 2020-12-25442Jayhawks3Phantoms5LSommaire du Match
66 - 2020-12-26449Jayhawks8Manchots2WSommaire du Match
68 - 2020-12-28466Jayhawks2Baby Hawks3LSommaire du Match
70 - 2020-12-30480Heat2Jayhawks6WSommaire du Match
72 - 2021-01-01495Baby Hawks1Jayhawks3WSommaire du Match
74 - 2021-01-03512Spiders4Jayhawks6WSommaire du Match
77 - 2021-01-06534Jayhawks3Sharks2WXXSommaire du Match
79 - 2021-01-08546Minnesota1Jayhawks9WSommaire du Match
82 - 2021-01-11567Jayhawks3Cougars1WSommaire du Match
83 - 2021-01-12576Jayhawks5Chill4WSommaire du Match
88 - 2021-01-17600Jayhawks5Las Vegas2WSommaire du Match
89 - 2021-01-18609Stars2Jayhawks3WSommaire du Match
91 - 2021-01-20625Chiefs4Jayhawks2LSommaire du Match
93 - 2021-01-22636Admirals1Jayhawks4WSommaire du Match
95 - 2021-01-24650Phantoms4Jayhawks2LSommaire du Match
98 - 2021-01-27664Jayhawks6Cabaret Lady Mary Ann1WSommaire du Match
100 - 2021-01-29680Jayhawks2Thunder1WSommaire du Match
101 - 2021-01-30690Jayhawks5Caroline4WSommaire du Match
103 - 2021-02-01706Manchots0Jayhawks5WSommaire du Match
105 - 2021-02-03724Sharks4Jayhawks3LXXSommaire du Match
107 - 2021-02-05739Jayhawks4Comets3WSommaire du Match
109 - 2021-02-07745Jayhawks3Oil Kings5LSommaire du Match
120 - 2021-02-18778Jayhawks6Admirals4WSommaire du Match
121 - 2021-02-19784Monarchs7Jayhawks5LSommaire du Match
123 - 2021-02-21800Baby Hawks3Jayhawks4WSommaire du Match
126 - 2021-02-24824Oil Kings4Jayhawks3LXSommaire du Match
128 - 2021-02-26838Caroline2Jayhawks4WSommaire du Match
130 - 2021-02-28844Jayhawks2Bruins3LXXSommaire du Match
132 - 2021-03-02860Jayhawks2Rocket4LSommaire du Match
133 - 2021-03-03867Jayhawks3Marlies6LSommaire du Match
135 - 2021-03-05885Jayhawks5Senators1WSommaire du Match
137 - 2021-03-07904Bears2Jayhawks3WSommaire du Match
139 - 2021-03-09917Sound Tigers4Jayhawks5WSommaire du Match
141 - 2021-03-11929Jayhawks3Stars2WSommaire du Match
142 - 2021-03-12938Jayhawks3Chiefs4LXSommaire du Match
144 - 2021-03-14954Thunder2Jayhawks3WXXSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17979Cabaret Lady Mary Ann2Jayhawks5WSommaire du Match
151 - 2021-03-211006Crunch5Jayhawks4LXXSommaire du Match
155 - 2021-03-251032Jayhawks3Comets7LSommaire du Match
157 - 2021-03-271046Jayhawks6Heat5WSommaire du Match
160 - 2021-03-301067Jayhawks1Oceanics3LSommaire du Match
163 - 2021-04-021092Comets4Jayhawks2LSommaire du Match
165 - 2021-04-041107Wolf Pack1Jayhawks4WSommaire du Match
167 - 2021-04-061122Stars4Jayhawks3LSommaire du Match
169 - 2021-04-081135Las Vegas1Jayhawks2WSommaire du Match
171 - 2021-04-101152Cougars2Jayhawks3WSommaire du Match
173 - 2021-04-121169Jayhawks4Monarchs2WSommaire du Match
176 - 2021-04-151190Jayhawks1Las Vegas3LSommaire du Match
179 - 2021-04-181214Chill4Jayhawks3LXXSommaire du Match
180 - 2021-04-191223Jayhawks-Sharks-
182 - 2021-04-211235Jayhawks-Monsters-
184 - 2021-04-231253Comets-Jayhawks-
186 - 2021-04-251259Oceanics-Jayhawks-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets5020
Assistance47,37325,575
Assistance PCT60.73%65.58%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
2 1870 - 62.35% 73,850$2,880,150$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,146,491$ 1,714,917$ 1,714,917$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
9,220$ 2,146,491$ 21 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
147,700$ 7 9,220$ 64,540$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT